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Hands-On Meta Learning with Python
Hands-On Meta Learning with Python

Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

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Profile Icon Sudharsan Ravichandiran
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (6 Ratings)
Paperback Dec 2018 226 pages 1st Edition
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Profile Icon Sudharsan Ravichandiran
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (6 Ratings)
Paperback Dec 2018 226 pages 1st Edition
eBook
$9.99 $35.99
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Hands-On Meta Learning with Python

Face and Audio Recognition Using Siamese Networks

In the last chapter, we learned about what meta learning is and different types of meta learning techniques. We also saw how to learn gradient descent by gradient descent and optimization as a model for few-shot learning. In this chapter, we will learn one of the most commonly used metric-based one-shot learning algorithms called siamese networks. We will see how siamese networks learn from very few data points and how they are used to solve the low data problem. After that we will explore the architecture of siamese networks in detail and we will see some of the applications of siamese networks. At the end of this chapter, we will learn how to build face and audio recognition models using siamese networks.

In this chapter, you will learn the following:

  • What are siamese networks?
  • Architecture of siamese networks
  • Applications of...

What are siamese networks?

A siamese network is a special type of neural network and it is one of the simplest and most popularly used one-shot learning algorithms. As we have learned in the previous chapter, one-shot learning is a technique where we learn from only one training example per class. So, a siamese network is predominantly used in applications where we don't have many data points in each class. For instance, let's say we want to build a face recognition model for our organization and about 500 people are working in our organization. If we want to build our face recognition model using a Convolutional Neural Network (CNN) from scratch, then we need many images of all of these 500 people for training the network and attaining good accuracy. But apparently, we will not have many images for all of these 500 people and so it is not feasible to build a model using...

Face recognition using siamese networks

We will understand the siamese network by building a face recognition model. The objective of our network is to understand whether two faces are similar or dissimilar. We use the AT&T Database of Faces, which can be downloaded from here: https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

Once you have downloaded and extracted the archive, you can see the folders s1, s2, up to s40, as shown here:

Each of these folders has 10 different images of a single person taken from various angles. For instance, let's open folder s1. As you can see, there are 10 different images of a single person:

We open and check folder s13:

As we know that siamese networks require input values as a pair along with the label, we have to create our data in such a way. So, we will take two images randomly from the same folder and mark...

Building an audio recognition model using siamese networks

In the last tutorial, we saw how to use siamese networks to recognize a face. Now we will see how to use siamese networks to recognize audio. We will train our network to differentiate between the sound of a dog and the sound of a cat. The dataset of cat and dog audio can be downloaded from here: https://www.kaggle.com/mmoreaux/audio-cats-and-dogs#cats_dogs.zip.

Once we have downloaded the data, we fragment our data into three folders: Dogs, Sub_dogs, and Cats. In Dogs and Sub_dogs, we place the dog's barking audio and in the Cats folder, we place the cat's audio. The objective of our network is to recognize whether the audio is a dog's barking or some different sound. As we know, for a siamese network, we need to feed input as a pair; we select an audio from the Dogs and Sub_dogs folders and mark them as...

Summary

In this chapter, we have learned what siamese networks are and how to build face and audio recognition models using siamese networks. We explored the architecture of siamese networks, which basically consists of two identical neural networks both having the same weights and architecture and the output of these networks is plugged into some energy function to understand the similarity.

In the next chapter, we will learn about prototypical networks and the variants of the same, such as Gaussian prototypical and semi prototypical networks. We will also see how to use prototypical networks for omniglot character set classification.

Questions

  1. What are siamese networks?
  2. What is the contrastive loss function?
  3. What is the energy function?
  4. What is the desired data format for a siamese network?
  5. What are the applications of siamese networks?

Further readings

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Key benefits

  • Understand the foundations of meta learning algorithms
  • Explore practical examples to explore various one-shot learning algorithms with its applications in TensorFlow
  • Master state of the art meta learning algorithms like MAML, reptile, meta SGD

Description

Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.

Who is this book for?

Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.

What you will learn

  • Understand the basics of meta learning methods, algorithms, and types
  • Build voice and face recognition models using a siamese network
  • Learn the prototypical network along with its variants
  • Build relation networks and matching networks from scratch
  • Implement MAML and Reptile algorithms from scratch in Python
  • Work through imitation learning and adversarial meta learning
  • Explore task agnostic meta learning and deep meta learning

Product Details

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Publication date : Dec 31, 2018
Length: 226 pages
Edition : 1st
Language : English
ISBN-13 : 9781789534207
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Product Details

Publication date : Dec 31, 2018
Length: 226 pages
Edition : 1st
Language : English
ISBN-13 : 9781789534207
Category :
Languages :

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Table of Contents

11 Chapters
Introduction to Meta Learning Chevron down icon Chevron up icon
Face and Audio Recognition Using Siamese Networks Chevron down icon Chevron up icon
Prototypical Networks and Their Variants Chevron down icon Chevron up icon
Relation and Matching Networks Using TensorFlow Chevron down icon Chevron up icon
Memory-Augmented Neural Networks Chevron down icon Chevron up icon
MAML and Its Variants Chevron down icon Chevron up icon
Meta-SGD and Reptile Chevron down icon Chevron up icon
Gradient Agreement as an Optimization Objective Chevron down icon Chevron up icon
Recent Advancements and Next Steps Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5
(6 Ratings)
5 star 33.3%
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2 star 16.7%
1 star 50%
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Anit Gupta Nov 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is very nice and the explanation is pretty clear and neat. I loved the model agnostic meta learning chapter as the author has broken down such complex topic into very simple explanation.One of the advantages of this book is it covers from the few short learning algorithms to the state of the art MAML algorithms.
Amazon Verified review Amazon
Vishnu Jan 17, 2020
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I stumbled upon this book's GitHub repo and bought this book. It's very well written and the algorithms are explained in the simplest way I could ever think of.Must Read for anyone willing to learn Meta-Learning!!!.
Amazon Verified review Amazon
龙猫 Nov 25, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Some of the theory is fairly good and clear for this book, but the code is an awful mess. There are not clear reasons why the author selected examples for certain examples. The code is also a mix of Python 2 and Python 3, so you need to have both installed. You could convert the code to Python 3, but that could be a lot of work and error prone. One problem with the text of the book is that the author used images for some of the symbols, and they don't render well for the kindle version. It is really hard to read them. I have tried reading on pc and on my phone, but some of the symbols are just impossible to read. There are some small problems with writing style, but those can be overlooked.
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Vishal Jun 15, 2020
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I do not recommend this book for serious readers. Most of his code comes from other's repositories on GitHub.
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Saee Razi Nov 27, 2019
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The code base does not work when you run it on your own machine, azure notebooks, or on google colab. When you look up errors you see that the writer copied and pasted code from the internet. This book is a ripoff. The writer has no clue what he is doing.
Amazon Verified review Amazon
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